Are you the assistant professor with experience in data analytics and business optimization? Maybe you are the right person to strengthen the further development of data analytics capabilities, both in research and in teaching, within the Chair group of Operations Research and Logistics (ORL).
These days research and education in the field of Operations Research and Logistics are shifting due to an increased focus on data analytics. Unfortunately, data availability in agri-food networks still varies. Data on business sciences increasingly become abundantly available at ever more granular levels. Many efforts are focused on generating even more granular data in networks (including agri-food networks, for example with the development of Smart Farming or the collection of detailed traceability data throughout the supply chain). The availability of increasingly granular data provides an important opportunity for more advanced decision making (in the face of trade-offs). Traditional empirical methods are not adequate to deal with this. Recent advances in machine learning approaches offer better opportunities to effectively exploit large datasets and combine approaches, for example: machine learning and optimization. As a result, decision-makers can have better insights into causes and effects of actions (and their interrelations - directly, indirectly and over time). These approaches may also allow for better interpretable models (instead of typical black-box models), which is increasingly demanded from data analytics approaches in business settings. Data analytics for situations where data is not (yet) abundantly available is equally relevant in the field of data analytics. There are still many situations where abundant data availability is a problem (e.g. with new product development and introductions or the use of new processing technologies). Prediction using such 'small data' is a field that is also becoming more and more relevant in data analytics and essential to feed optimization models aiming to provide prescriptive analytics.
The ORL group is active in teaching at both the Bachelor, Master and PhD level. The successful candidate may support existing courses as well as develop new courses that fit the profile of the group. The successful candidate is furthermore expected to conduct data driven research that is focused on and inspired by industry applications, using advanced statistical skills for processing and analyzing datasets programmed in software such as Python or R. Candidates who have experience with connecting machine learning and optimization approaches are especially welcome.
Your key responsibilities:
- establish a research portfolio that is embedded within the Operations Research and Logistics group;
- undertake research that leads to high quality research outputs;
- lecture courses (lectures, practicals) on Data Driven Supply Chain Management and Decision Science for Technology and develop new educational materials;
- supervise BSc and MSc thesis students, as well as PhD students where possible;
- submit relevant grant applications in line with the group's research focus and obtain research funding;
- communicate outcomes of research to relevant partners.